Safe Haskell	None
Language	Haskell2010

DataFrame.LinearModel.Regression

Description

Linear regression with the standard penalties: OLS (QR), ridge (Cholesky), and lasso/elastic net (FISTA). fit produces a LinearRegressor; predict compiles it to an Expr Double over the raw feature columns.

Synopsis

module DataFrame.Model
data Penalty
- = OLS
- | Ridge !Double
- | Lasso !Double
- | ElasticNet !Double !Double
data LinearConfig = LinearConfig {
- lcPenalty :: !Penalty
- lcSolver :: !SolverConfig
}
defaultLinearConfig :: LinearConfig
data LinearRegressor = LinearRegressor {
- regCoef :: !(Vector Double)
- regIntercept :: !Double
- regFeatureNames :: !(Vector Text)
- regPenalty :: !Penalty
}

Documentation

module DataFrame.Model

data Penalty Source #

Regularization choice. alpha is the penalty strength; l1Ratio mixes L1/L2.

Constructors

OLS
Ridge !Double
Lasso !Double
ElasticNet !Double !Double

Instances

Instances details

Show Penalty Source #
Instance details Defined in DataFrame.LinearModel.Regression Methods showsPrec :: Int -> Penalty -> ShowS # show :: Penalty -> String # showList :: [Penalty] -> ShowS #
Eq Penalty Source #
Instance details Defined in DataFrame.LinearModel.Regression Methods (==) :: Penalty -> Penalty -> Bool # (/=) :: Penalty -> Penalty -> Bool #

data LinearConfig Source #

Hyperparameters for linear regression: the penalty and the FISTA solver config.

Constructors

LinearConfig
Fields lcPenalty :: !Penalty lcSolver :: !SolverConfig

Instances

Instances details

Show LinearConfig Source #

Instance details

Defined in DataFrame.LinearModel.Regression

Methods

showsPrec :: Int -> LinearConfig -> ShowS #

show :: LinearConfig -> String #

showList :: [LinearConfig] -> ShowS #

Eq LinearConfig Source #

Instance details

Defined in DataFrame.LinearModel.Regression

Methods

(==) :: LinearConfig -> LinearConfig -> Bool #

(/=) :: LinearConfig -> LinearConfig -> Bool #

SegmentFit LinearConfig Double Source #

Linear segments with exact closed-form pooling. λ = 0 is independent OLS; λ > 0 shrinks each segment's coefficients toward the n_g-weighted mean of the per-segment fits.

Instance details

Defined in DataFrame.Segmented

Methods

fitSegments :: LinearConfig -> Double -> Expr Double -> [DataFrame] -> [ModelOf LinearConfig (Expr Double)] Source #

Fit LinearConfig (Expr Double) Source #

Instance details

Defined in DataFrame.LinearModel.Regression

Associated Types

type ModelOf LinearConfig (Expr Double)
Instance details Defined in DataFrame.LinearModel.Regression type ModelOf LinearConfig (Expr Double) = LinearRegressor
type FrameReq LinearConfig (Expr Double)
Instance details Defined in DataFrame.LinearModel.Regression type FrameReq LinearConfig (Expr Double) = 'AllDoubleFrame